CN110085056A - Vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings - Google Patents
Vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings Download PDFInfo
- Publication number
- CN110085056A CN110085056A CN201910333606.XA CN201910333606A CN110085056A CN 110085056 A CN110085056 A CN 110085056A CN 201910333606 A CN201910333606 A CN 201910333606A CN 110085056 A CN110085056 A CN 110085056A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- lane
- target vehicle
- target
- conflict
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings, comprising steps of 1) selected target vehicle, obtains relevant information；2) coordinate system is established by origin of target vehicle, and is evenly divided into m × n grid cell；3) target vehicle lane-change trajectory predictions model, displacement and position during calculating target vehicle lane-change are established；4) surrounding vehicles motion profile prediction model is established, displacement and position of the surrounding vehicles during target vehicle lane-change are calculated；5) vehicle lane-changing conflict set recognition model is established, the conflict condition of different lane-change conflict types is specified；6) lane-change risk class is divided based on kinetic energy rejection to divide, indicated with different colours；7) vehicle lane-changing risk identification is realized by computer programming, and exports potential lane-change risk class and its corresponding color.The present invention can solve the problems, such as the identification in advance of intelligent vehicle lane-change risk under highway bus or train route cooperative surroundings.
Description
Technical field
The present invention relates to the technical fields of vehicle on highway lane-change safety, refer in particular to a kind of highway bus or train route association
With vehicle lane-changing instantaneous risk recognition methods under environment.
Background technique
Loss brought by traffic accidents is much higher than ordinary road with lethality, trace it to its cause mainly due to
Highway speeds are high, once traffic accident occurs, the huge kinetic energy of vehicle high-speed collision release be enough to vehicle with drive
Sail generation that is artificial at severe trauma, while being easy therefore cause the secondary collisions event such as continuous rear collision.Due to highway row
The relative closure of vehicle, lane changing and lane keep being the typical two kinds of driving behavior modes of highway, with traffic safety
It is closely coupled.Therefore, instantaneous risk when vehicle executes lane changing is accurately identified in advance, helps to improve vehicle early warning system
The technical system of system, improves vehicle safety in operation, and the safe operation for intelligent vehicle under bus or train route cooperative surroundings provides theoretical base
Plinth and technical support.
Currently, most of vehicle lane-changing research is concentrated mainly on based on current lane-change safety condition assessment vehicle lane-changing safety
Property, and the influence research to surrounding vehicles Future movement track rarely has concern, ignore during lane-change uncertain factor and
Influence of the reciprocation to vehicle lane-changing safety between vehicle, to reduce the discrimination of lane-change risk.It in addition, there will be research more
The real-time for focusing on risk identification, and has ignored the predictability of risk, to the identification of risk there are significant time lag, and it is difficult
To identify lane-change risk in advance and carry out early warning to driver there are problems in practical applications.
In consideration of it, vehicle on highway lane-change risk identification problem is converted potential punching during vehicle lane-changing by the present invention
Bump identification problem, consideration target vehicle and the Future movement state of surrounding vehicles, the laterally and longitudinally factors such as motion profile, really
Determine conflict condition, establish vehicle lane-changing conflict set recognition model, portrays lane-change conflict seriousness from the angle of capacity loss, and carry out
Lane-change risk class divides.The present invention can effectively identify vehicle lane-changing collision risk in advance, for vehicle early warning system, drive automatically
It sails vehicle lane-changing control and theoretical reference and technical support is provided.
Summary of the invention
The purpose of the present invention is to overcome the shortcomings of the existing technology and insufficient, proposes a kind of highway bus or train route collaboration ring
Vehicle lane-changing instantaneous risk recognition methods under border passes through if establishing coordinate system as origin using target vehicle and being divided into dry lattice
It predicts the motion profile of target vehicle and surrounding vehicles, identifies the potential conflict point of target vehicle and surrounding vehicles.Meanwhile in order to
Effectively identification vehicle lane-changing risk, proposes the vehicle lane-changing risk class division methods for considering conflict severity, with synthesis
Vehicle lane-changing level of security is assessed, and visualizes output vehicle lane-changing risk class.
To achieve the above object, technical solution provided by the present invention are as follows: a kind of highway bus or train route cooperative surroundings are got off
Lane-change instantaneous risk recognition methods, comprising the following steps:
1) selected target vehicle obtains road geometrical condition, vehicle geometric parameter, target vehicle motion state data, week
Enclose state of motion of vehicle data, surrounding traffic condition and surface friction coefficient；
2) two-dimensional Cartesian system is established by origin of target vehicle, and is evenly divided into m × n grid cell,
To occur the phenomenon that vehicle in each grid cell is considered as at grid cell center and vehicle occurs；
3) target vehicle lane-change trajectory predictions model, displacement and position during calculating target vehicle lane-change are established；
4) establish surrounding vehicles motion profile prediction model, calculate displacement of the surrounding vehicles during target vehicle lane-change with
Position；
5) vehicle lane-changing conflict set recognition model is established, the conflict condition of different lane-change conflict types is specified；
6) size of conflict kinetic energy rejection is calculated, and the division of lane-change risk class is carried out based on this, is indicated with different colours；
7) vehicle lane-changing risk identification is realized by computer programming, and exports potential lane-change risk class and its corresponding
Color.
In step 1), the road geometrical condition includes lane width and number of track-lines, can pass through vehicle-mounted navigation map
Or video identification obtains；The vehicle geometric parameter includes Vehicle length and vehicle width, can pass through reading body electronics mark
It signs information or video identification obtains；The target vehicle motion state data includes the speed, acceleration of target vehicle, vehicle position
It sets and vehicle body and lane middle line angle when lane-change, target vehicle sensor can be passed through and obtain vehicle real-time motion data；It is described
Surrounding vehicles motion state data includes speed, acceleration, vehicle location and the vehicle body and lane middle line angle of surrounding vehicles, energy
Surrounding vehicles real-time motion data is enough obtained by the sensor of target vehicle or bus or train route collaborative platform；The surrounding traffic condition
Including the target vehicle vehicle spacing with target lane front truck, rear car respectively, can be assisted from the sensor or bus or train route of target vehicle
It is obtained with platform；Surface friction coefficient includes the surface friction coefficient in the case of different weather, can pass through expert method or correlation
Standard setting.
In step 2), coordinate system is established using target vehicle as origin and grid division is carried out to it, comprising the following steps:
2.1) establish coordinate system: using target vehicle as origin, target vehicle direction of travel is that Y-axis is positive, and target vehicle is right
Side direction is that X-axis is positive, establishes plane right-angle coordinate；
2.2) using target vehicle as origin, m-1, n-1 uniformly grid division: are inserted into plane coordinate system X-axis and Y-axis
Target vehicle travel zone is divided into m × n grid by point, and wherein m, n are positive integer and its value must make each
Grid length and width are respectively less than vehicle width, to reflect the arrival of vehicle, specific value view highway cross section width with
Depending on the speed limit condition of section, then have:
- M=x1< x2< ... < xj< ... < xm< xm+1=M
- N=y1< y2< ... < yi< ... < yn< yn+1=N
In formula, xjFor the abscissa of jth column branch, yiFor the i-th row branch ordinate, if cross section width is 2M, when conflict
The maximum travel distance of interior target vehicle is N；
2.3) approximation is handled: will occur the phenomenon that vehicle in each grid cell is considered as at grid cell center and vehicle occurs
, then the grid of the i-th row jth column is denoted as Rec (xij,yij), centroid coordinate (xij,yij) it is denoted as:
In formula: xjFor the abscissa of jth column branch, yiFor the ordinate of the i-th row branch.
In step 3), according to target vehicle motion state and initial position, predict to be displaced during target vehicle lane-change
With position, comprising the following steps:
3.1) target vehicle is calculated in the length travel of t moment, i.e. Y direction is displaced, it is known that target vehicle present speed
v0, acceleration a0, lane-change when vehicle body and lane middle line angle theta and preceding k seconds of acceleration ak, then target vehicle is in t moment
Length travel are as follows:
vy=v0cosθ
ay=a0cosθ
In formula, △ ayLongitudinal acceleration for target vehicle in t moment changes average value；SyIt is target vehicle in t moment
Length travel, unit: m；vyFor the longitudinal velocity at target vehicle current time, unit: m/s；ayFor target vehicle current time
Longitudinal acceleration, unit: m/s2；
3.2) lateral displacement of the target vehicle in t moment is calculated, i.e. X-direction is displaced, and vehicle adds in the transverse direction of t moment
Speed is described by following formula:
Integral can obtain the lateral displacement size during vehicle lane-changing are as follows:
In formula, tcFor target vehicle to the total time that lane-change is completed, unit: s since lane-change；D is lane width, single
Position: m；SxIt is target vehicle in the lateral displacement of t moment, vehicle is negative value to left dislocation, and vehicle is displaced to the right as positive value, single
Position: m；axFor the transverse acceleration of t moment target vehicle, unit: m/s2；
3.3) target vehicle is calculated in the position of t moment, it is known that initial position G (x0, the y of target vehicle0) and t moment
Transverse and longitudinal is displaced, then position G (x of the target vehicle in t momentt,yt) are as follows:
xt=x0+Sx
yt=y0+Sy
In formula, x0For the lateral coordinates of target vehicle initial position；y0For the longitudinal coordinate of target vehicle initial position；xt
For the lateral coordinates of target vehicle t moment；ytFor the longitudinal coordinate of target vehicle t moment；SxFor target vehicle t moment cross
To displacement, vehicle is negative value to left dislocation, and vehicle is displaced to the right as positive value, unit: m；SyIt is target vehicle in the longitudinal direction of t moment
Displacement, unit: m.
In step 4), according to surrounding vehicles motion state and initial position, surrounding vehicles are calculated in target vehicle lane-change
The displacement of period and position, comprising the following steps:
4.1) surrounding vehicles are calculated in t moment position Gh(xht,yht) longitudinal coordinate, it is known that the initial position of surrounding vehicles
Gh(xh0,yh0), present speed vh, acceleration ah, vehicle body and lane middle line angle thetahAnd first k seconds of acceleration ahk, then its
In the longitudinal coordinate y of t momenthtAre as follows:
vhy=vhcosθh
ahy=ahcosθh
yht=yh0+Shy
In formula, h=a or b respectively indicate target lane front truck, rear car；△ahyAdd for surrounding vehicles in the longitudinal direction of t moment
Velocity variations average value；yh0For the longitudinal coordinate of surrounding vehicles initial position；yhtIt is sat for the longitudinal direction of surrounding vehicles t moment position
Mark；ShyLength travel for surrounding vehicles in t moment, unit: m；vhyFor the longitudinal velocity at surrounding vehicles current time, unit:
m/s；ahyFor the longitudinal acceleration at surrounding vehicles current time, unit: m/s2；
4.2) surrounding vehicles are calculated in t moment position Gh(xht,yht) lateral coordinates xht；It can similarly obtain, surrounding vehicles exist
The lateral coordinates of t moment are xht:
vhx=vhsinθh
ahx=ahsinθh
xht=xh0+Shx
In formula, h=a or b respectively indicate target lane front truck, rear car；△ahxTransverse direction for surrounding vehicles in t moment adds
Velocity variations average value；ahkFor preceding k seconds of acceleration, unit: m/s2；xh0For the lateral coordinates of surrounding vehicles initial position；xht
For the lateral coordinates of surrounding vehicles t moment position；ShxIt is surrounding vehicles in the lateral displacement of t moment, vehicle is negative to left dislocation
Value, vehicle are displaced to the right as positive value, unit: m；vhxFor the lateral velocity at surrounding vehicles current time, unit: m/s；ahxFor week
The transverse acceleration at vehicle current time is enclosed, unit: m/s2；θhFor the angle of vehicle body and lane middle line.
In step 5), by grid cell, the spacing of target vehicle and surrounding vehicles is less than criticality safety within the conflict time
The moment of spacing is considered as a lane-change conflict, specifies the conflict condition of different lane-change conflict types, establishes vehicle lane-changing conflict knowledge
Other model, comprising the following steps:
5.1) determine conflict time t: traffic conflict is traffic behavior, and person is clearly felt that accident hazard exists, and takes
The traffic of active and effective hedging behavior meets with event, and traffic conflict is defined as two motor vehicles in lane-change time t hereincIt is interior
Vehicle spacing is less than the phenomenon that criticality safety spacing, then the time t that conflicts indicates that target vehicle completes the time t of lane-change processc, instead
Reflect target vehicle during lane-change whether with surrounding vehicles generate traffic conflict；
T=tc
5.2) lane-change conflict condition differentiates: according to the difference of lane-change conflict object, defining target vehicle and surrounding vehicle respectively
Lane-change conflict condition, to identify lane-change conflict；Lane-change conflict is mainly striven by target vehicle and target lane fore-aft vehicle
Take the generation of path space resource by force, therefore can be by the difference of conflict object, the lane-change for being divided into target vehicle and target lane front truck is rushed
Prominent, target vehicle conflicts with the lane-change of target lane rear car；
1. target vehicle conflicts with the lane-change of target lane front truck: known target vehicle-to-target lane front truck it is initial between
Away from for hs1, criticality safety spacing is SaIf hs1<SaThen it is considered as that there are potential conflicts；If hs1≥Sa, then its there are potential conflicts
Condition are as follows:
vty=vy+(ay+Δay)t
yat-0.5La≤yt+Sa+0.5L
In formula, vtyFor the longitudinal velocity of target vehicle t moment, unit: m/s；vyFor the longitudinal direction at target vehicle current time
Speed, unit: m/s；△ayLongitudinal acceleration for target vehicle in t moment changes average value；ayFor target vehicle current time
Longitudinal acceleration, unit: m/s2；For different surface friction coefficients；yatIt is sat for the longitudinal direction of target lane front truck t moment position
Mark；ytFor the longitudinal coordinate of target vehicle t moment position；LaFor the Vehicle length of target lane front truck, unit: m；L is target carriage
Vehicle length, unit: m；
2. target vehicle conflicts with the lane-change of target lane rear car: known target vehicle-to-target lane rear car it is initial between
Away from for hs2, criticality safety spacing is SbIf hs2<SbThen it is considered as that there are potential conflicts；If hs2≥Sb, then its there are potential conflicts
Condition are as follows:
vbty=vby+(aby+Δaby)t
yt-0.5L≤ybt+Sb+0.5Lb
In formula, vbtyFor the longitudinal velocity of target lane rear car t moment, unit: m/s；vbyWhen current for target lane rear car
The longitudinal velocity at quarter, unit: m/s；△abyLongitudinal acceleration for target lane rear car in t moment changes average value；abyFor mesh
Mark the longitudinal acceleration at lane rear car current time, unit: m/s2；For different surface friction coefficients；ybtBehind target lane
The longitudinal coordinate of vehicle t moment position；ytFor the longitudinal coordinate of target vehicle t moment position；LbVehicle for target lane rear car is long
Degree, unit: m；L is the Vehicle length of target vehicle, unit: m.
In step 6), the size of conflict kinetic energy rejection is calculated, and the division of lane-change risk class is carried out based on this, with difference
Color expression, specifically includes the following steps:
6.1) severity calculates: kinetic energy rejection refers to kinetic energy change amount of the vehicle before and after experience conflict, size
Be able to reflect the vehicle active force and speed loss suffered in lane-change conflict, it is assumed hereinafter that under the conditions of:
1. vehicle mass is close；
2. generate lane-change conflict between vehicle, default rear car is decelerated to until being slower than front truck, and front truck is unaffected；
Under rectangular coordinate system, with the energy loss characterization conflict severity of vehicle lane-changing conflict, that is, each grid is calculated
Interior vehicle deceleration to preceding vehicle speed unit mass kinetic energy loss below, expression formula are as follows:
In formula, h=a or b respectively indicate target lane front truck, rear car；△KEmeanAfter front vehicle experience conflict
Mean kinetic energy loss, unit J/kg；M is the quality of front vehicle, unit: kg；vty、vhtyIt is target vehicle and surrounding respectively
Longitudinal velocity when vehicle collision, unit: m/s；
6.2) severity level divides: according to the data statistics of collision loss kinetic energy as a result, severity is divided into
Slight conflict, general conflict, more serious conflict and four grades of Serious conflicts, respectively with green, yellow, orange carried out with red
Intuitive display, and conflict severity is divided based on this.
Compared with prior art, the present invention have the following advantages that with the utility model has the advantages that
1, it proposes vehicle lane-changing instantaneous risk recognition methods under highway bus or train route cooperative surroundings, is conducive to identify vehicle in advance
Lane-change conflict, the problems such as to avoid lane-change accident.
2, it is different from previous vehicle lane-changing security evaluation research, the present invention is based on target vehicles and the following of surrounding vehicles to transport
Dynamic state identifies existing potential conflict point during lane-change.It solves the problems, such as the time lag identified based on real-time lane-change danger state, mentions
The comprehensive and reliability of high vehicle lane-changing security evaluation.
3, break through only by the lane-change danger state recognition methods of condition distinguishing, the present invention using target vehicle as coordinate origin,
It identifies its lane-change existing potential conflict point in the process, and the division of lane-change risk class is carried out based on conflict severity, for
The improvement of vehicle lane-changing early warning is of great significance.
4, vehicle lane-changing security situation is expressed using visual image form, security level is distinguished by color, image is straight
It sees, so that driver is no longer limited to obtain lane-change security information by phonetic warning mode, it can be with more intuitively, efficiently
Mode perceives lane-change risk, has stronger practical value.
Detailed description of the invention
Fig. 1 is the modeling procedure frame diagram of the method for the present invention.
Fig. 2 is coordinate system of the present invention and grid division schematic diagram.
Fig. 3 is vehicle movement trajectory predictions schematic diagram of the present invention.
Fig. 4 is vehicle lane-changing of the present invention conflict type and criterion schematic diagram.
Specific embodiment
The present invention is further explained in the light of specific embodiments.
As shown in Figure 1, vehicle lane-changing instantaneous risk identification side under highway bus or train route cooperative surroundings provided by the present invention
Method, comprising the following steps:
1) selected target vehicle obtains road geometrical condition, vehicle geometric parameter, target vehicle motion state data, week
Enclose state of motion of vehicle data, surrounding traffic condition and surface friction coefficient.
The road geometrical condition includes lane width, number of track-lines, can be obtained by vehicle-mounted navigation map or video identification；
The vehicle geometric parameter includes Vehicle length, vehicle width, can be obtained by reading body electronics label information or video identification
It takes；The target vehicle motion state data includes the speed, acceleration of target vehicle, vehicle location, vehicle body corner, can be passed through
Target vehicle sensor obtains vehicle real-time motion data；The surrounding vehicles motion state data includes the speed of surrounding vehicles
Degree, acceleration, vehicle location, vehicle body corner can obtain surrounding vehicles by the sensor or bus or train route collaborative platform of target vehicle
Real-time motion data；The surrounding traffic condition includes vehicle spacing of the target vehicle respectively with target lane front truck, rear car, can
It is obtained from the sensor or bus or train route collaborative platform of target vehicle；Surface friction coefficient includes the pavement friction in the case of different weather
Coefficient can be set by expert method or relevant criterion.
Road geometrical condition are as follows: number of track-lines 3, lane width 3.75m, N=5.625m, M=100m, m=10, n=
50。
Vehicle geometric parameter are as follows: assuming that all vehicles are car, then have Vehicle length L=La=Lb=4m, vehicle are wide
Spend W=Wa=Wb=2.5m.
Target vehicle motion state data are as follows: initial velocity V0=23m/s；Initial acceleration a0=1.2m/s2；Initial bit
It is set to G (0,0)；Vehicle body and lane middle line angle theta=30 ° when lane-change；K=2 is enabled, then a1=1.0m/s2, a2=0.8m/s2。
Surrounding vehicles motion state data are as follows: the initial velocity V of target lane front trucka=27m/s, initial acceleration aa=
0.3m/s2, initial position is G (- 4,48), vehicle body and lane middle line angle thetaa=0 °, k=2 is enabled, then aa1=-0.1m/s2, aa2
=-0.5m/s2；The initial velocity V of target lane rear carb=26m/s, initial acceleration ab=0.8m/s2, initial position be G (-
4, -69), vehicle body and lane middle line angle thetab=0 °, k=2 is enabled, then ab1=0.5m/s2, ab2=0.3m/s2。
Surrounding traffic condition are as follows: the initial vehicle spacing of target vehicle and target lane front truck is hs1=44m；Target vehicle
Initial vehicle spacing with target lane rear car is hs2=65m.
Surface friction coefficient are as follows: assuming that weather conditions are dry weather, highway is cement pavement, then coefficient of friction
2) coordinate system is established according to target vehicle current location and grid processing is carried out to it, as shown in Fig. 2, including following
Step:
2.1) coordinate system is established.Using target vehicle as origin, target vehicle driving direction is that Y-axis is positive, and target vehicle is right
Side direction is that X-axis is positive, establishes plane right-angle coordinate；
2.2) grid division.If m=10, n=50, merging area is evenly dividing as 10 × 50 500 grids totally.Wherein xj
For the abscissa of jth column branch, yiFor the i-th row branch ordinate, then have:
- 5.625=x1< x2< ... < xj< ... < x9< x10=5.625
- 100=y1< y2< ... < yi< ... < y49< y50=100
2.3) approximation is handled: will occur the phenomenon that vehicle in each grid cell is considered as at grid cell center and vehicle occurs
, then the grid of the i-th row jth column is denoted as Rec (xij,yij), centroid coordinate (xij,yij) it is denoted as:
3) according to target vehicle motion state and initial position, the displacement and position during target vehicle lane-change are predicted,
See Fig. 3.Wherein, if conflict time t=tc=3s.
3.1) target vehicle is calculated in the length travel of t moment.Known target vehicle present speed v0, acceleration a0, vehicle body
With the angle theta and first 2 seconds acceleration a of lane middle line1、a2, then length travel of the target vehicle in t moment are as follows:
vy=23 × cos30 °=19.92m/s
ay=1.2 × cos30 °=1.04m/s2
3.2) lateral displacement of the target vehicle in t moment is calculated, i.e. X-direction is displaced.Vehicle is in the lateral position of t moment
It moves are as follows:
Sx=-3.75m
3.3) target vehicle is calculated in the position of t moment.The cross of initial position G (0,0) and t moment of known target vehicle
Length travel, then position G (x of the target vehicle in t momentt,yt) are as follows:
xt=-3.75
yt=74.45
It follows that target vehicle is G (- 3.75,74.45) in the position in t moment, corresponding grid coordinate be (- 4,
17)。
4) according to surrounding vehicles motion state and initial position, displacement of the surrounding vehicles during target vehicle lane-change is calculated
With position.
4.1) the longitudinal coordinate y of surrounding vehicles t moment is calculatedht。
For the front truck of target lane, the lengthwise position y of t momentatIt calculates as follows:
vay=27 × cos0 °=27m/s
aay=0.3 × cos0 °=0.3m/s2
yat=48+84.15=132.15
For the rear car of target lane, the lengthwise position y of t momentbtIt calculates as follows:
vby=26 × cos0 °=26m/s
aby=0.8 × cos0 °=0.8m/s2
ybt=-69+82.73=13.73
4.2) surrounding vehicles are calculated in the lateral coordinates x of t momentht。
For the front truck of target lane, the lateral position x of t momentatIt calculates as follows:
vax=27 × sin0 °=0m/s
aax=0.3 × sin0 °=0m/s2
Sax=0m
xat=-4
For the rear car of target lane, the lateral position x of t momentbtIt calculates as follows:
vbx=26 × sin0 °=0m/s
abx=0.8 × sin0 °=0m/s2
Sbx=0m
xbt=-4
In conclusion the coordinate position of target lane front truck t moment is Ga(- 4,132.15), corresponding grid coordinate be (-
4,34)；The coordinate position of target lane rear car t moment is Ga(- 4,13.73), corresponding grid coordinate are (- 4,5).
5) differentiate that target vehicle lane-change whether there is potential conflict point, as shown in Figure 4.
The differentiation 1. target vehicle conflicts with the lane-change of target lane front truck.
vty=19.92+ (1.04+0.17) × 3=23.55m/s
Due to 44 > 29.54, i.e. hs1> Sa, current vehicle spacing meets criticality safety spacing, then needs further to judge it
Whether potential conflict condition is met.
yat-0.5La=132.15-2=130.15
yt+Sa+ 0.5L=74.45+29.54+2=105.99
Due to 130.15 > 105.99, therefore target vehicle conflicts with target lane front truck there is no potential lane-change.
The differentiation 2. target vehicle conflicts with the lane-change of target lane rear car.
vbty=26+ (0.8+0.25) × 3=29.15m/s
Due to 65 > 61.94, i.e. hs2> Sb, current vehicle spacing meets criticality safety spacing, then needs further to judge it
Whether potential conflict condition is met.
yt- 0.5L=74.45-2=72.45
ybt+Sb+0.5Lb=13.73+61.94+2=77.63
Due to 72.45 < 77.63, then there are potential lane-changes to conflict with target lane rear car for target vehicle.
6) size of prominent kinetic energy rejection is calculated, and the division of lane-change risk class is carried out based on this, indicates have with different colours
Body the following steps are included:
6.1) severity calculates.Kinetic energy rejection refers to kinetic energy change amount of the vehicle before and after experience conflict, size
It is able to reflect the vehicle active force and speed loss suffered in lane-change conflict.It is assumed hereinafter that under the conditions of:
1. vehicle mass is close；
2. generate lane-change conflict between vehicle, default rear car is decelerated to until being slower than front truck, and front truck is unaffected；
Under rectangular coordinate system, with the energy loss characterization conflict severity of vehicle lane-changing conflict, that is, each grid is calculated
Interior vehicle deceleration to preceding vehicle speed unit mass kinetic energy loss below, expression formula are as follows:
In formula, h=a or b respectively indicate target lane front truck, rear car；△KEmeanAfter front vehicle experience conflict
Mean kinetic energy loss, unit J/kg；M is the quality of front vehicle, unit: kg；vty、vhtyIt is target vehicle and surrounding respectively
Longitudinal velocity when vehicle collision, unit: m/s.
6.2) severity level divides.Lane-change risk class is divided based on conflict kinetic energy rejection, and grid is assigned
Value.According to existing kinetic energy rejection statistical data, severity is divided into slight conflict, general conflict, more serious conflict and tight
Conflict four grades again, respectively with green, yellow, it is orange intuitively shown with red, it is as shown in table 1:
1 risk class of table divides
| Risk class | 1 | 2 | 3 | 4 |
| Conflict kinetic energy rejection (J/kg) | 0~50 | 50~100 | 100~150 | ≥150 |
| Color | Green | Yellow | It is orange | It is red |
Then target vehicle conflicts kinetic energy rejection with target lane rear car are as follows: Δ KEmean=147.56J/kg, risk class are
3, belong to more serious conflict, is indicated with orange.
7) vehicle lane-changing risk identification is realized by computer programming, and exports potential lane-change risk class and its corresponding
Color.
Embodiment described above is only the preferred embodiments of the invention, and but not intended to limit the scope of the present invention, therefore
All shapes according to the present invention change made by principle, should all be included within the scope of protection of the present invention.
Claims (7)
1. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings, which is characterized in that including following
Step:
1) selected target vehicle obtains road geometrical condition, vehicle geometric parameter, target vehicle motion state data, surrounding vehicle
Motion state data, surrounding traffic condition and surface friction coefficient；
2) two-dimensional Cartesian system is established by origin of target vehicle, and is evenly divided into m × n grid cell, it will be every
Occurring the phenomenon that vehicle in a grid cell is considered as at grid cell center and vehicle occurs；
3) target vehicle lane-change trajectory predictions model, displacement and position during calculating target vehicle lane-change are established；
4) surrounding vehicles motion profile prediction model is established, displacement and position of the surrounding vehicles during target vehicle lane-change are calculated
It sets；
5) vehicle lane-changing conflict set recognition model is established, the conflict condition of different lane-change conflict types is specified；
6) size of conflict kinetic energy rejection is calculated, and the division of lane-change risk class is carried out based on this, is indicated with different colours；
7) vehicle lane-changing risk identification is realized by computer programming, and exports potential lane-change risk class and its corresponding face
Color.
2. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings according to claim 1,
It is characterized by: in step 1), the road geometrical condition includes lane width and number of track-lines, can by vehicle mounted guidance
Figure or video identification obtain；The vehicle geometric parameter includes Vehicle length and vehicle width, can pass through reading body electronics
Label information or video identification obtain；The target vehicle motion state data includes the speed, acceleration of target vehicle, vehicle
Vehicle body and lane middle line angle when position and lane-change can obtain vehicle real-time motion data by target vehicle sensor；Institute
The speed, acceleration, vehicle location and vehicle body and lane middle line angle that surrounding vehicles motion state data includes surrounding vehicles are stated,
Surrounding vehicles real-time motion data can be obtained by the sensor or bus or train route collaborative platform of target vehicle；The surrounding traffic item
Part includes vehicle spacing of the target vehicle respectively with target lane front truck, rear car, can be from the sensor or bus or train route of target vehicle
Collaborative platform obtains；Surface friction coefficient includes the surface friction coefficient in the case of different weather, can pass through expert method or phase
Close standard setting.
3. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings according to claim 1,
It is characterized by: establishing coordinate system in step 2) using target vehicle as origin and carrying out grid division, including following step to it
It is rapid:
2.1) establish coordinate system: using target vehicle as origin, target vehicle direction of travel is that Y-axis is positive, target vehicle right side side
To for X-axis forward direction, establish plane right-angle coordinate；
2.2) grid division: using target vehicle as origin, being uniformly inserted into m-1, n-1 points in plane coordinate system X-axis and Y-axis, will
Target vehicle travel zone is divided into m × n grid, and wherein m, n are positive integer and its value must make each grid
Length and width are respectively less than vehicle width, to reflect the arrival of vehicle, specific value view highway cross section width and section
Depending on speed limit condition, then have:
- M=x1< x2< ... < xj< ... < xm< xm+1=M
- N=y1< y2< ... < yi< ... < yn< yn+1=N
In formula, xjFor the abscissa of jth column branch, yiConflict in the time for the i-th row branch ordinate if cross section width is 2M
The maximum travel distance of target vehicle is N；
2.3) approximation is handled: will occur the phenomenon that vehicle in each grid cell is considered as at grid cell center and vehicle occurs,
Then the grid of the i-th row jth column is denoted as Rec (xij,yij), centroid coordinate (xij,yij) it is denoted as:
In formula: xjFor the abscissa of jth column branch, yiFor the ordinate of the i-th row branch.
4. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings according to claim 1,
It is characterized by: according to target vehicle motion state and initial position, predicting position during target vehicle lane-change in step 3)
Move and position, comprising the following steps:
3.1) target vehicle is calculated in the length travel of t moment, i.e. Y direction is displaced, it is known that target vehicle present speed v0Plus
Speed a0, lane-change when vehicle body and lane middle line angle theta and preceding k seconds of acceleration ak, then target vehicle is in the longitudinal direction of t moment
Displacement are as follows:
vy=v0cosθ
ay=a0cosθ
In formula, △ ayLongitudinal acceleration for target vehicle in t moment changes average value；SyIt is target vehicle in the longitudinal direction of t moment
Displacement, unit: m；vyFor the longitudinal velocity at target vehicle current time, unit: m/s；ayFor the longitudinal direction at target vehicle current time
Acceleration, unit: m/s2；
3.2) lateral displacement of the target vehicle in t moment is calculated, i.e. X-direction is displaced, transverse acceleration of the vehicle in t moment
It is described by following formula:
Integral can obtain the lateral displacement size during vehicle lane-changing are as follows:
In formula, tcFor target vehicle to the total time that lane-change is completed, unit: s since lane-change；D is lane width, unit: m；Sx
It is target vehicle in the lateral displacement of t moment, vehicle is negative value to left dislocation, and vehicle is displaced to the right as positive value, unit: m；axFor
The transverse acceleration of t moment target vehicle, unit: m/s2；
3.3) target vehicle is calculated in the position of t moment, it is known that the initial position G (x of target vehicle0,y0) with the transverse and longitudinal of t moment
To displacement, then position G (x of the target vehicle in t momentt,yt) are as follows:
xt=x0+Sx
yt=y0+Sy
In formula, x0For the lateral coordinates of target vehicle initial position；y0For the longitudinal coordinate of target vehicle initial position；xtFor mesh
Mark the lateral coordinates of vehicle t moment；ytFor the longitudinal coordinate of target vehicle t moment；SxIt is target vehicle in the lateral position of t moment
It moves, vehicle is negative value to left dislocation, and it is positive value, unit: m that vehicle, which is displaced to the right,；SyIt is target vehicle in longitudinal position of t moment
It moves, unit: m.
5. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings according to claim 1,
It is characterized by: according to surrounding vehicles motion state and initial position, calculating surrounding vehicles in step 4) and being changed in target vehicle
Displacement and position during road, comprising the following steps:
4.1) surrounding vehicles are calculated in t moment position Gh(xht,yht) longitudinal coordinate, it is known that the initial position G of surrounding vehiclesh
(xh0,yh0), present speed vh, acceleration ah, vehicle body and lane middle line angle thetahAnd first k seconds of acceleration ahk, then it is in t
The longitudinal coordinate y at momenthtAre as follows:
vhy=vhcosθh
ahy=ahcosθh
yht=yh0+Shy
In formula, h=a or b respectively indicate target lane front truck, rear car；△ahyFor surrounding vehicles t moment longitudinal acceleration
Change average value；yh0For the longitudinal coordinate of surrounding vehicles initial position；yhtFor the longitudinal coordinate of surrounding vehicles t moment position；Shy
Length travel for surrounding vehicles in t moment, unit: m；vhyFor the longitudinal velocity at surrounding vehicles current time, unit: m/s；
ahyFor the longitudinal acceleration at surrounding vehicles current time, unit: m/s2；
4.2) surrounding vehicles are calculated in t moment position Gh(xht,yht) lateral coordinates xht；It can similarly obtain, surrounding vehicles are in t moment
Lateral coordinates be xht:
vhx=vhsinθh
ahx=ahsinθh
xht=xh0+Shx
In formula, h=a or b respectively indicate target lane front truck, rear car；△ahxFor surrounding vehicles t moment transverse acceleration
Change average value；ahkFor preceding k seconds of acceleration, unit: m/s2；xh0For the lateral coordinates of surrounding vehicles initial position；xhtFor week
Enclose the lateral coordinates of vehicle t moment position；ShxIt is surrounding vehicles in the lateral displacement of t moment, vehicle is negative value to left dislocation, vehicle
Displacement to the right is positive value, unit: m；vhxFor the lateral velocity at surrounding vehicles current time, unit: m/s；ahxFor surrounding vehicles
The transverse acceleration at current time, unit: m/s2；θhFor the angle of vehicle body and lane middle line.
6. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings according to claim 1,
It is characterized by: in step 5), by grid cell within the conflict time spacing of target vehicle and surrounding vehicles be less than it is critical
The moment of safe spacing is considered as a lane-change conflict, specifies the conflict condition of different lane-change conflict types, establishes vehicle lane-changing punching
Prominent identification model, comprising the following steps:
5.1) determine conflict time t: traffic conflict is traffic behavior, and person is clearly felt that accident hazard exists, and takes actively
The traffic of effective hedging behavior meets with event, and traffic conflict is defined as two motor vehicles in lane-change time t hereincInterior vehicle
Spacing is less than the phenomenon that criticality safety spacing, then the time t that conflicts indicates that target vehicle completes the time t of lane-change processc, reflect mesh
Mark vehicle during lane-change whether with surrounding vehicles generate traffic conflict；
T=tc
5.2) lane-change conflict condition differentiates: being conflicted the difference of object according to lane-change, defines target vehicle and surrounding vehicles respectively
Lane-change conflict condition, to identify lane-change conflict；Lane-change conflict mainly fights for road by target vehicle and target lane fore-aft vehicle
Road space resources generate, therefore can by conflict object difference, be divided into target vehicle conflict with the lane-change of target lane front truck, mesh
Mark the lane-change conflict of vehicle-to-target lane rear car；
1. target vehicle conflicts with the lane-change of target lane front truck: the initial spacing of known target vehicle-to-target lane front truck is
hs1, criticality safety spacing is SaIf hs1<SaThen it is considered as that there are potential conflicts；If hs1≥Sa, then its there are the conditions of potential conflict
Are as follows:
vty=vy+(ay+Δay)t
yat-0.5La≤yt+Sa+0.5L
In formula, vtyFor the longitudinal velocity of target vehicle t moment, unit: m/s；vyFor the longitudinal velocity at target vehicle current time,
Unit: m/s；△ayLongitudinal acceleration for target vehicle in t moment changes average value；ayFor indulging for target vehicle current time
To acceleration, unit: m/s2；For different surface friction coefficients；yatFor the longitudinal coordinate of target lane front truck t moment position；yt
For the longitudinal coordinate of target vehicle t moment position；LaFor the Vehicle length of target lane front truck, unit: m；L is target vehicle
Vehicle length, unit: m；
2. target vehicle conflicts with the lane-change of target lane rear car: the initial spacing of known target vehicle-to-target lane rear car is
hs2, criticality safety spacing is SbIf hs2<SbThen it is considered as that there are potential conflicts；If hs2≥Sb, then its there are the conditions of potential conflict
Are as follows:
vbty=vby+(aby+Δaby)t
yt-0.5L≤ybt+Sb+0.5Lb
In formula, vbtyFor the longitudinal velocity of target lane rear car t moment, unit: m/s；vbyFor target lane rear car current time
Longitudinal velocity, unit: m/s；△abyLongitudinal acceleration for target lane rear car in t moment changes average value；abyFor target carriage
The longitudinal acceleration at road rear car current time, unit: m/s2；For different surface friction coefficients；ybtWhen for target lane rear car t
Carve the longitudinal coordinate of position；ytFor the longitudinal coordinate of target vehicle t moment position；LbIt is single for the Vehicle length of target lane rear car
Position: m；L is the Vehicle length of target vehicle, unit: m.
7. vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings according to claim 1,
It is characterized by: calculating the size of conflict kinetic energy rejection in step 6), and carry out the division of lane-change risk class based on this, using
Different colours expression, specifically includes the following steps:
6.1) severity calculates: kinetic energy rejection refers to kinetic energy change amount of the vehicle before and after experience conflict, and size can
Reflect the vehicle active force and speed loss suffered in lane-change conflict, it is assumed hereinafter that under the conditions of:
1. vehicle mass is close；
2. generate lane-change conflict between vehicle, default rear car is decelerated to until being slower than front truck, and front truck is unaffected；
Under rectangular coordinate system, with the energy loss characterization conflict severity of vehicle lane-changing conflict, that is, vehicle in each grid is calculated
It is decelerated to and preceding vehicle speed unit mass kinetic energy loss below, expression formula are as follows:
In formula, h=a or b respectively indicate target lane front truck, rear car；△KEmeanIt is average dynamic after front vehicle experience conflict
The loss of energy, unit J/kg；M is the quality of front vehicle, unit: kg；vty、vhtyIt is that target vehicle and surrounding vehicles are rushed respectively
Longitudinal velocity when prominent, unit: m/s；
6.2) severity level divides: according to the data statistics of collision loss kinetic energy as a result, severity is divided into slightly
Conflict, general conflict, more serious conflict and four grades of Serious conflicts, respectively with green, yellow, it is orange with it is red carry out it is intuitive
It has been shown that, and conflict severity is divided based on this.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910333606.XA CN110085056B (en) | 2019-04-24 | 2019-04-24 | Method for identifying instantaneous risk of vehicle lane change in expressway vehicle-road cooperative environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910333606.XA CN110085056B (en) | 2019-04-24 | 2019-04-24 | Method for identifying instantaneous risk of vehicle lane change in expressway vehicle-road cooperative environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110085056A true CN110085056A (en) | 2019-08-02 |
| CN110085056B CN110085056B (en) | 2021-06-08 |
Family
ID=67416540
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910333606.XA Active CN110085056B (en) | 2019-04-24 | 2019-04-24 | Method for identifying instantaneous risk of vehicle lane change in expressway vehicle-road cooperative environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110085056B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110555476A (en) * | 2019-08-29 | 2019-12-10 | 华南理工大学 | intelligent vehicle track change track prediction method suitable for man-machine hybrid driving environment |
| CN110675656A (en) * | 2019-09-24 | 2020-01-10 | 华南理工大学 | Intelligent vehicle lane change early warning method based on instantaneous risk identification |
| CN110930733A (en) * | 2019-11-29 | 2020-03-27 | 腾讯科技（深圳）有限公司 | Lane switching risk processing method and device, storage medium and computer equipment |
| CN111469847A (en) * | 2020-04-14 | 2020-07-31 | 宁波吉利汽车研究开发有限公司 | Lane change path planning method and system |
| CN112233413A (en) * | 2020-07-20 | 2021-01-15 | 北方工业大学 | Multilane space-time trajectory optimization method for intelligent networked vehicle |
| CN112258893A (en) * | 2020-09-01 | 2021-01-22 | 华南理工大学 | Intelligent vehicle lane change collision probability assessment method based on track prediction |
| CN112965489A (en) * | 2021-02-05 | 2021-06-15 | 北京理工大学 | Intelligent vehicle high-speed lane change planning method based on collision detection |
| CN113096379A (en) * | 2021-03-03 | 2021-07-09 | 东南大学 | Driving style identification method based on traffic conflict |
| CN113147794A (en) * | 2021-06-03 | 2021-07-23 | 北京百度网讯科技有限公司 | Method, device and equipment for generating automatic driving early warning information and automatic driving vehicle |
| CN113240901A (en) * | 2021-03-05 | 2021-08-10 | 东南大学 | Intelligent vehicle lane changing behavior risk level determination method and device |
| CN113313949A (en) * | 2021-05-31 | 2021-08-27 | 长安大学 | Method, device and equipment for cooperative control of passenger cars and trucks on expressways and ramp ways |
| WO2023050749A1 (en) * | 2021-09-30 | 2023-04-06 | 上海商汤智能科技有限公司 | Track prediction method and apparatus, electronic device, and storage medium |
| CN115953917A (en) * | 2023-03-14 | 2023-04-11 | 西安科技大学 | Dangerous road-changing section identification and control method based on real-time track characteristic analysis |
| CN116504088A (en) * | 2023-05-24 | 2023-07-28 | 合肥工业大学 | Expressway vehicle lane change decision method considering safety risk in networking environment |
| CN117315938A (en) * | 2023-10-19 | 2023-12-29 | 广州市城市规划勘测设计研究院 | Expressway vehicle lane change risk estimation method, medium and equipment |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006050879A1 (en) * | 2004-11-12 | 2006-05-18 | Daimlerchrysler Ag | Method for operating a vehicle provided with a collision avoidance system and device for carrying out such a method |
| CN102800214A (en) * | 2012-08-27 | 2012-11-28 | 武汉大学 | Vehicle lane change conflict resolution method under vehicle information interaction condition |
| CN103496366A (en) * | 2013-09-09 | 2014-01-08 | 北京航空航天大学 | Active-lane-changing collision-avoidance control method and device based on vehicle-vehicle coordination |
| CN104882025A (en) * | 2015-05-13 | 2015-09-02 | 东华大学 | Crashing detecting and warning method based on vehicle network technology |
| CN105741551A (en) * | 2016-04-22 | 2016-07-06 | 华南理工大学 | Method for evaluating traffic safety of unsignalized intersection |
| CN105966396A (en) * | 2016-05-13 | 2016-09-28 | 江苏大学 | Vehicle collision avoidance control method based on driver collision avoidance behavior |
| CN106926779A (en) * | 2017-03-09 | 2017-07-07 | 吉利汽车研究院（宁波）有限公司 | A kind of vehicle lane change accessory system |
| CN108062600A (en) * | 2017-12-18 | 2018-05-22 | 北京星云互联科技有限公司 | A kind of vehicle collision prewarning method and device based on rectangle modeling |
| CN108387242A (en) * | 2018-02-07 | 2018-08-10 | 西南交通大学 | Automatic Pilot lane-change prepares and executes integrated method for planning track |
| CN108399740A (en) * | 2018-01-22 | 2018-08-14 | 华南理工大学 | A kind of signalized crossing motor vehicle collision probability prediction technique |
| CN109353337A (en) * | 2018-08-10 | 2019-02-19 | 重庆大数据研究院有限公司 | A kind of intelligent vehicle lane-change stage collision probability safety predicting method |
| CN109389864A (en) * | 2017-08-09 | 2019-02-26 | 湖南大学 | A kind of vehicle relative positioning and anti-collision warning method |
-
2019
- 2019-04-24 CN CN201910333606.XA patent/CN110085056B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006050879A1 (en) * | 2004-11-12 | 2006-05-18 | Daimlerchrysler Ag | Method for operating a vehicle provided with a collision avoidance system and device for carrying out such a method |
| CN102800214A (en) * | 2012-08-27 | 2012-11-28 | 武汉大学 | Vehicle lane change conflict resolution method under vehicle information interaction condition |
| CN103496366A (en) * | 2013-09-09 | 2014-01-08 | 北京航空航天大学 | Active-lane-changing collision-avoidance control method and device based on vehicle-vehicle coordination |
| CN104882025A (en) * | 2015-05-13 | 2015-09-02 | 东华大学 | Crashing detecting and warning method based on vehicle network technology |
| CN105741551A (en) * | 2016-04-22 | 2016-07-06 | 华南理工大学 | Method for evaluating traffic safety of unsignalized intersection |
| CN105966396A (en) * | 2016-05-13 | 2016-09-28 | 江苏大学 | Vehicle collision avoidance control method based on driver collision avoidance behavior |
| CN106926779A (en) * | 2017-03-09 | 2017-07-07 | 吉利汽车研究院（宁波）有限公司 | A kind of vehicle lane change accessory system |
| CN109389864A (en) * | 2017-08-09 | 2019-02-26 | 湖南大学 | A kind of vehicle relative positioning and anti-collision warning method |
| CN108062600A (en) * | 2017-12-18 | 2018-05-22 | 北京星云互联科技有限公司 | A kind of vehicle collision prewarning method and device based on rectangle modeling |
| CN108399740A (en) * | 2018-01-22 | 2018-08-14 | 华南理工大学 | A kind of signalized crossing motor vehicle collision probability prediction technique |
| CN108387242A (en) * | 2018-02-07 | 2018-08-10 | 西南交通大学 | Automatic Pilot lane-change prepares and executes integrated method for planning track |
| CN109353337A (en) * | 2018-08-10 | 2019-02-19 | 重庆大数据研究院有限公司 | A kind of intelligent vehicle lane-change stage collision probability safety predicting method |
Non-Patent Citations (6)
| Title |
|---|
| HONGXUE XU ET AL.: "A Cellular Automata Traffic Flow Model Based on Safe Lane-Changing Distance Constraint Rule", 《IEEE》 * |
| KLAUS W.SCHMIDT: "Cooperative Adaptive Cruise Control for Vehicle Following During Lane Changes", 《EL SEVIER》 * |
| 廖明军 等: "《高速公路》", 31 October 2013 * |
| 罗强: "面向高速公路行车安全预警的车道偏离及换道模型研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 许伦辉 等: "基于最小安全距离的车辆换道模型研究", 《广西师范大学学报:自然科学版》 * |
| 陈友荣 等: "基于车车通信的车辆防碰撞算法", 《电信科学》 * |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110555476A (en) * | 2019-08-29 | 2019-12-10 | 华南理工大学 | intelligent vehicle track change track prediction method suitable for man-machine hybrid driving environment |
| CN110555476B (en) * | 2019-08-29 | 2023-09-26 | 华南理工大学 | Intelligent vehicle lane change track prediction method suitable for man-machine hybrid driving environment |
| CN110675656A (en) * | 2019-09-24 | 2020-01-10 | 华南理工大学 | Intelligent vehicle lane change early warning method based on instantaneous risk identification |
| CN110675656B (en) * | 2019-09-24 | 2020-09-22 | 华南理工大学 | Intelligent vehicle lane change early warning method based on instantaneous risk identification |
| CN110930733B (en) * | 2019-11-29 | 2021-07-30 | 腾讯科技（深圳）有限公司 | Lane switching risk processing method and device, storage medium and computer equipment |
| CN110930733A (en) * | 2019-11-29 | 2020-03-27 | 腾讯科技（深圳）有限公司 | Lane switching risk processing method and device, storage medium and computer equipment |
| CN111469847A (en) * | 2020-04-14 | 2020-07-31 | 宁波吉利汽车研究开发有限公司 | Lane change path planning method and system |
| CN111469847B (en) * | 2020-04-14 | 2022-06-14 | 宁波吉利汽车研究开发有限公司 | Lane change path planning method and system |
| CN112233413A (en) * | 2020-07-20 | 2021-01-15 | 北方工业大学 | Multilane space-time trajectory optimization method for intelligent networked vehicle |
| CN112233413B (en) * | 2020-07-20 | 2022-03-29 | 北方工业大学 | Multilane space-time trajectory optimization method for intelligent networked vehicle |
| CN112258893A (en) * | 2020-09-01 | 2021-01-22 | 华南理工大学 | Intelligent vehicle lane change collision probability assessment method based on track prediction |
| CN112258893B (en) * | 2020-09-01 | 2021-10-26 | 华南理工大学 | Intelligent vehicle lane change collision probability assessment method based on track prediction |
| CN112965489A (en) * | 2021-02-05 | 2021-06-15 | 北京理工大学 | Intelligent vehicle high-speed lane change planning method based on collision detection |
| CN113096379A (en) * | 2021-03-03 | 2021-07-09 | 东南大学 | Driving style identification method based on traffic conflict |
| CN113240901A (en) * | 2021-03-05 | 2021-08-10 | 东南大学 | Intelligent vehicle lane changing behavior risk level determination method and device |
| CN113313949A (en) * | 2021-05-31 | 2021-08-27 | 长安大学 | Method, device and equipment for cooperative control of passenger cars and trucks on expressways and ramp ways |
| CN113313949B (en) * | 2021-05-31 | 2022-03-25 | 长安大学 | Method, device and equipment for cooperative control of passenger cars and trucks on expressways and ramp ways |
| CN113147794A (en) * | 2021-06-03 | 2021-07-23 | 北京百度网讯科技有限公司 | Method, device and equipment for generating automatic driving early warning information and automatic driving vehicle |
| WO2023050749A1 (en) * | 2021-09-30 | 2023-04-06 | 上海商汤智能科技有限公司 | Track prediction method and apparatus, electronic device, and storage medium |
| CN115953917A (en) * | 2023-03-14 | 2023-04-11 | 西安科技大学 | Dangerous road-changing section identification and control method based on real-time track characteristic analysis |
| CN116504088A (en) * | 2023-05-24 | 2023-07-28 | 合肥工业大学 | Expressway vehicle lane change decision method considering safety risk in networking environment |
| CN116504088B (en) * | 2023-05-24 | 2024-03-26 | 合肥工业大学 | Expressway vehicle lane change decision method considering safety risk in networking environment |
| CN117315938A (en) * | 2023-10-19 | 2023-12-29 | 广州市城市规划勘测设计研究院 | Expressway vehicle lane change risk estimation method, medium and equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110085056B (en) | 2021-06-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110085056A (en) | Vehicle lane-changing instantaneous risk recognition methods under a kind of highway bus or train route cooperative surroundings | |
| EP3604066B1 (en) | Method, apparatus and system for controlling vehicle-following speed and storage medium | |
| CN107933551B (en) | A kind of intelligence fleet lane-change method | |
| CN110155046A (en) | Automatic emergency brake hierarchical control method and system | |
| CN110164183A (en) | A kind of safety assistant driving method for early warning considering his vehicle driving intention under the conditions of truck traffic | |
| US20220036735A1 (en) | Real-time driving risk assessment method employing equivalent force and device thereof | |
| CN107867283A (en) | Integrated form FCW/ACC/AEB systems and vehicle based on forecast model | |
| CN107274722A (en) | A kind of traffic safety early warning system and method towards using mobile phone pedestrian | |
| CN110723141B (en) | Vehicle active collision avoidance system and collision avoidance mode switching method thereof | |
| CN110155045A (en) | A kind of vehicle promptly accelerates Self-Protection Subsystem and its self-protection method | |
| CN109849910A (en) | Automatic driving vehicle multiobjective decision-making control method, device and computer readable storage medium | |
| CN105501221B (en) | A kind of self-adaption cruise system and control method based on truck traffic | |
| CN111942433B (en) | Method, system and device for protecting safety of cooperative formation train | |
| CN106564497A (en) | Apparatus and method of controlling main vehicle | |
| CN103587524A (en) | Lateral active collision avoidance system and control method thereof | |
| CN109823339A (en) | Vehicle traffic light intersection passing control method and control system | |
| CN110723142B (en) | Intelligent automobile emergency collision avoidance control method | |
| CN109334668B (en) | Automatic doubling system based on V2X | |
| CN112590812A (en) | Local path planning state switching method based on automatic driving | |
| CN109781129A (en) | A kind of road surface safety detection system and method based on inter-vehicular communication | |
| CN109823349A (en) | A kind of implementation method interacted for automatic driving vehicle with the external world | |
| CN109501798B (en) | Travel control device and travel control method | |
| CN113192331A (en) | Intelligent early warning system and early warning method for riding safety in internet environment | |
| US11332139B2 (en) | System and method of controlling operation of autonomous vehicle | |
| CN113428180A (en) | Method, system and terminal for controlling single-lane running speed of unmanned vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |